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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### A Quick Example\n",
      "Here is a quick example of what it looks like. I am writing this in my notebook file by the way (using a Mardown cell).\n",
      "\n",
      "Here is some code + output:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# have to comment out the magic IPython functions because of a bug\n",
      "#%pylab inline\n",
      "import numpy as np\n",
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print np.random.randn(20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[-0.81256785 -1.16630768  0.27555802  0.57729188 -0.64691411  0.62288591\n",
        "  0.27943851  0.10512695  0.23808598 -1.45293996 -0.24394825 -0.14631097\n",
        "  1.56377514 -0.87629984  1.64059433 -0.10259616  0.84435183  0.11718899\n",
        " -0.66617413 -0.81800771]\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.Series(np.random.randn(100)).plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7f28246381d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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bclMB6Jj9/daPvnoIYR6xjxxJ4q09tvq7TJhAF/e773qP2PXqoq+PPufUY9+x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\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f2824626b10>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "\n",
      "Raw NBConvert Cells\n",
      "-------------------\n",
      "\n",
      "In addition to markdown cells, you may also use **Raw NBConvert** cells. This allows you to input reST directly in your notebook. It won't look as pretty in the notebook itself, but will work great for your docs. Special reST and Sphinx markup will work within a Raw NBConvert (link, code markup, etc.).\n",
      "\n",
      "By the way, :func:`convert_notebooks <klink.__init__.convert_notebooks>` basically calls::\n",
      "\n",
      "    ipython nbconvert --to rst\n",
      "\n",
      "on all the notebooks it finds. It also handles special cases and adds special css classes for display formatting purposes. If you plan on using IPython Notebook integration, you will need an up-to-date version of `pandoc <http://johnmacfarlane.net/pandoc/>`_ for this to work properly."
     ]
    }
   ],
   "metadata": {}
  }
 ]
}